§1.1 Field of the Invention
The present invention concerns advertising, such as online advertising for example. In particular, the present invention concerns helping an advertising system to allow different advertisers, with different value-propositions, to compete against one another for various ad spots.
§1.2 Background Information
Advertising using traditional media, such as television, radio, newspapers and magazines, is well known. Unfortunately, even when armed with demographic studies and entirely reasonable assumptions about the typical audience of various media outlets, advertisers recognize that much of their ad budget is simply wasted. Moreover, it is very difficult to identify and eliminate such waste.
Recently, advertising over more interactive media has become popular. For example, as the number of people using the Internet has exploded, advertisers have come to appreciate media and services offered over the Internet as a potentially powerful way to advertise.
Interactive advertising provides opportunities for advertisers to target their ads to a receptive audience. That is, targeted ads are more likely to be useful to end users since the ads may be relevant to a need inferred from some user activity (e.g., relevant to a user's search query to a search engine, relevant to content in a document requested by the user, etc.). Query keyword targeting has been used by search engines to deliver relevant ads. For example, the AdWords advertising system by Google Inc. of Mountain View, Calif. (referred to as “Google”), delivers ads targeted to keywords from search queries. Similarly, content targeted ad delivery systems have been proposed. For example, U.S. patent application Ser. No. 10/314,427 (incorporated herein by reference and referred to as “the '427 application”), titled “METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS”, filed on Dec. 6, 2002 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as inventors; and Ser. No. 10/375,900 (incorporated by reference and referred to as “the '900 application”), titled “SERVING ADVERTISEMENTS BASED ON CONTENT,” filed on Feb. 26, 2003 and listing Darrell Anderson, Paul Buchheit, Alex Carobus, Claire Cui, Jeffrey A. Dean, Georges R. Harik, Deepak Jindal and Narayanan Shivakumar as inventors, describe methods and apparatus for serving ads relevant to the content of a document, such as a Web page for example. Content targeted ad delivery systems, such as the AdSense advertising system by Google for example, have been used to serve ads on Web pages.
As can be appreciated from the foregoing, serving ads relevant to concepts of text in a text document and serving ads relevant to keywords in a search query are useful because such ads presumably concern a current user interest. Consequently, such online advertising has become increasingly popular. Moreover, advertising using other targeting techniques, and even untargeted online advertising, has become increasingly popular. However, such advertising systems still have room for improvement.
For example, different kinds of advertisers have different value propositions. Consider, for example, three (3) different advertisers intended to be representative of broad segments. Assume that, representative of the first broad segment, advertiser NIKE is primarily interested in building their brand and is most interested in impressions on recognizable publisher Websites that they know their target audience visits. Assume that, representative of the second broad segment, advertiser WINZIP is primarily interested in getting users to download and install their software which is available from the landing page of their ad. WINZIP is therefore most interested in ad selections (sometimes referred to in the specification as “clicks” without loss of generality). Finally, assume that, representative of the third broad segment, advertiser BOOKCLUB wants users to buy books from their Website and is willing to share specific conversion data when purchases are made.
Thus, from the perspective of an advertising network, NIKE reports a very non-specific value, perhaps because it expects impressions (also referred to as “view-throughs”) to lead to conversions that it cannot track effectively (or that it does not wish to share with the advertising network). WINZIP concerns itself with more specific value (i.e., clicks). BOOKCLUB focuses on the most specific value (i.e., actual purchases by the user). Suppose that the perceived value proposition for these advertisers is as follows:
NIKE: $1 per 1,000 impressions (i.e., $1 CPM);
WINZIP: $0.50 per click; and
BOOKCLUB: $1-$20 per book purchase, depending on the book.
As will be illustrated below, current advertising networks are typically focused on capturing one type of value proposition, to the detriment of the other two.
Consider first, an advertising network that accepts cost per selection offers from advertisers, and that charges advertisers when users actually click on their ads. These offers may be referred to as “cost per click” or “CPC” offers. The current AdWords and AdSense advertising networks from Google are examples of such an ad network. Naturally, this type of advertising network works well for WINZIP, because it matches WINZIP's value proposition closely. WINZIP can offer $0.50 per click, and end up paying $0.50 (or less if discounted) for each click.
Unfortunately, however, this type of advertising network does not work well for NIKE and BOOKCLUB. More specifically, although NIKE derives its value from impressions, it needs to model (or convert) its value to a cost per selection offer. Specifically, NIKE would need to derive a CPC offer from a CPM offer that matches its value proposition. It could do so by using a selection rate (referred to as “click-through rate” or “CTR” in the specification without loss of generality). For example, CPM can be estimated as CPC*CTR, so NIKE can simply solve for CPC and get CPC=CPM/CTR. While NIKE knows what it wants as its CPM, CTR is beyond its control, and is potentially unstable. Consequently, NIKE is faced with a management challenge. Specifically, if NIKE's CPM value is estimated to be constant, as CTR goes down, its starts offering (and paying) less for those impressions. Conversely, as CTR goes up, it may end up offering too much and over-paying for impressions. To avoid making offers that are too high or too low, NIKE must use CTR (e.g., as reported to it by the advertising network) to adjust its offer on a regular basis to make their estimated CPM relatively constant to match their value.
BOOKCLUB also has a problem. It may have offline-data (or use conversion tracking) to understand how frequently a click leads to a purchase, and to know how valuable those purchases are to it. Its value per click may be estimated to be the weighted sum of products of the conversion rate*conversion values. BOOKCLUB can compute a CPC offer from conversation rate and conversion values. However, as the conversion rates change, BOOKCLUB will, like NIKE, have to regularly update their CPC offer to reflect the most current data in order to avoid offering too much or too little.
As can be appreciated from the foregoing example, an advertising network that only accepts cost per selection offers from advertisers would be useful to WINZIP, but would cause problems (e.g., in terms of ease of management of an ad campaign) for NIKE and BOOKCLUB.
Now consider an advertising network where advertisers make offers (e.g., bids) strictly for impressions. These offers may be referred to as “cost per (thousand) impressions” or “CPM” offers. In such an ad network, it would be easier for NIKE to manage its ad campaign because the form of the offer matches its value proposition. That is, NIKE would simply bid $1.00 CPM. However, since WINZIP measures its value in terms of selections (e.g., clicks), it would need to convert its CPC value to a CPM offer. It could determine its CPM based on the CTR and their value per click using the formula CPM=CPC*CTR. To avoid making offers that are too high or too low, WINZIP must use CTR (e.g., as reported to it by the advertising network) to adjust its offer on a regular basis to make their estimated CPM relatively constant to match their value. BOOKCLUB has to do something similar, but needs to make its determination over the weighted sum of CTR*CONVERSION_RATE*VALUE for all of their conversion data. In this scenario, WINZIP and BOOKCLUB are forced to regularly update their CPM offer in order to reflect their value proposition.
As can be appreciated from the foregoing example, an advertising network that only accepts cost per (thousand) impression offers from advertisers would be useful to NIKE, but would cause problems (e.g., in terms of ease of management of an ad campaign) for WINZIP and BOOKCLUB.
Finally, consider an advertising network in which advertisers make offers (e.g., bids) for conversions. These offers may be referred to as “cost per acquisition” or “CPA” offers. For this ad network, to avoid overpaying or underpaying, NIKE would have to use observed data about CTR or other conversion rates to back-compute the value of those conversions to reflect their value on impressions. Similarly, WINZIP would have to use observed data about conversion rates (from clicks) to back-compute the value of those conversions to reflect their value on selections (clicks). With this system, BOOKCLUB gets to make offers (bids) that reflect its value directly. As can be appreciated from the foregoing example, an advertising network that accepts only cost per acquisition offers from advertisers would be useful to BOOKCLUB, but would cause problems (e.g., in terms of ease of management of an ad campaign) for NIKE and WINZIP.
As the exemplary scenarios involving three (3) different types of advertising networks and three (3) different types of advertisers illustrate, different advertisers have different value propositions that are modeled better with certain types of offers than they can be modeled with other types of offers. Thus, an improved advertising network would be useful.
Another problem with some existing advertising networks is that some advertisers value impressions on so-called top-tier publications (e.g., The New York Times, Sports Illustrated, etc.) (much) more than impressions of other publications. For example, NIKE may want to be able to pay a premium to get impressions on top-tier publishers. The dual problem facing top-tier publishers is that they are unable to monetize their publication's (e.g., Website's) brand (relative to the masses of other less prominent publications) using existing advertising network products. For example, human judgment is often used to determine the price paid for pay-per-impression ads (e.g., often based on the type of audience attracted to a Website as well and the likelihood that the ad will reach its intended audience). Often, when advertisers buy ad placements from large publishers, they are shown the places their ads will run and a direct sales force negotiates a price based on the inventory viewed. Currently it is required that people on behalf of the Web publisher and the advertiser negotiate a price.
The foregoing customs of pay-per-impression advertising have a number of disadvantages. First, to be diligent, the advertiser must review each Website and go through laborious negotiations for each Website, and possibly each placement, to set the price to be paid for ad impressions. This human involvement and per channel pricing does not scale to allow purchase—on a price per impression basis—of ad spots displayed on a large network of Websites.
To avoid this scalability problem, many large advertising networks sell ads on a CPC basis. Unfortunately, as illustrated by the example above, CPC advertising networks do not serve the needs of so-called “brand” advertisers, who may just want to get a message across without requiring a click (e.g. “Watch Alias. Now on Wed. nights on ABC”, or “Diet Pepsi—Light! Crisp! Refreshing!”) well, nor do they serve the needs of advertisers like BOOKCLUB well.
Consequently, top-tier premium publishers may be reluctant to join advertising networks such as the AdSense advertising network from Google because they are unable to extract value from advertisers beyond what the rest of the network receives. As mentioned above, some brand advertisers have the dual problem in that they wish to pay a premium to run on certain publications. Some ad networks currently do not allow advertisers to express their desire for specific publications (e.g., Websites) or vertical segments, and therefore cannot collect additional revenue to provide the improved monetization to those premium publishers. Therefore, it would be useful to improve these existing advertising networks.
It would be useful if such an improved advertising network allowed different advertisers with different value propositions to be able to directly express (i.e., without the need to track selection rates, conversion rates, etc. and recalculate offers based on these rates) their value propositions. It would be useful if such an improved advertising network allowed advertisers to offer more for placement (or selection, or conversion, or some other event) on certain (e.g., top-tier, premium) publications, or for particular vertical segments. At the same time, it would be useful if such an improved advertising system would allow advertisers to choose the level of detail at which they wish to express their value. For example, advertisers should remain free to keep conversion data private. It would also be useful for such an improved advertising network to give greater control to advertisers who desire it, yet maintain simplicity for advertisers who prefer ease of use.